In wireless communications the channel between the network nodes (e.g., terminals, base stations, access points, remote radio heads, user equipments, etc.) varies over time and frequency. The receivers at the receiving nodes are arranged in a way to be able to track the radio channel variations in the time-frequency grid over which the data is transmitted. In most cases, the receiver structure is optimized by assuming that the perfect channel state information (CSI) (i.e. the channel gain) or an estimate of the CSI with sufficiently high fidelity is available at the receiver side.
This assumption leads to several important consequences in order to design the transmitter-receiver chain. Most notably, under the assumption of perfect CSI, a receiver that employs the nearest-neighbour decoding, is optimal which allows the system operates to its theoretical performance limit. With the nearest-neighbour decision rule, the decoder attempts to choose a codeword which is closest to the received noisy signal vector (i.e., sampled base band signals) in some norms, for example Euclidean distance for Gaussian channels or Hamming distance for binary channels. This decoding rule requires the decoder to scale the transmitted codeword with the channel gain. In practice the channel gain is not available beforehand and should be learned. The CSI can be learned either implicitly or explicitly from the received noisy signals.
Channel estimation is a crucial component in the receiver chain for coherent data transmission and detection. The coherent reception means that the receiver can compensate for the phase rotation and magnitude amplifications of the transmitted modulated symbols. The channel estimation enables the receiver to track the received coded and modulated signal. This approach generally results in a simpler modular system design for data detection at the receiver side: channel estimation followed by data detection. To furnish the channel estimation at the receiver, some pre-specified transmission symbols, referred to as pilot or reference symbols, are multiplexed with data symbols (i.e. information-bearing symbols) at the transmitter. That is, the transmission of each block of coded symbols is divided into sub-blocks such that over each sub-block the transmitter consumes resources for learning the channel as well as transmitting data.
The receiver using the known transmitted pilot symbols estimates the channel gain between transmit and receive antennas. Thereafter, the estimated channel is utilized to perform decoding of the received data packet. Pilot transmission is of common practice in many standardized wireless communication systems, for example that of Long Term Evolution (LTE). In general, this method of data transmission and reception is referred to as pilot-assisted communications.
For example, the pilot symbols in LTE are distributed in the time-frequency grid of the radio resources. The location (i.e., time-frequency mapping) and value of the pilot symbols are pre-specified in each system such as in LTE. The receiver using the knowledge of the transmitted pilot symbols and the associated time-frequency mapping finds the equivalent complex channel (i.e. phase rotation and magnitude amplification) that affected the transmitted pilot symbols. These estimated channels are then utilized to estimate the channel gain affecting the transmitted data symbols. This step is generally performed by interpolation of the estimated channel over the pilot symbols.
The pilot-assisted communication, despite its simple implementation, suffers from two main issues: loss in the spectral efficiency due the transmission of pilot symbols and propagation of the channel estimation errors to the decoder at the receiver.
FIG. 1 illustrates the conventional pilot-assisted transmitter chain. At the transmitter the transmission data (i.e., raw information bits) generated by a communication source are grouped using the segmentation block and then additional bits in form of for example cyclic redundancy check (CRC) are added to each information block. Then each information block are passed to a channel encoder to generate a packet of coded bits. The shown encoder block in FIG. 1 typically consists of a mother error correction coder followed by rate matching and possibly a bit-interleaver. The coded information bits are next passed to a modulator to produce coded symbols. Finally, the coded symbols are multiplexed with predefined pilot symbols and then mapped to time-frequency resource elements using a multiplexer to be transmitted over the physical channel. The rate-matching is used to allow the flexibly to match the coded information bits to the number available resources at the transmitter. The main purpose of the CRC is to enable the receiver to verify whether the decoded packet is correct or not. In case, the added CRC does not correctly check after the decoding, the receiver may initiate a Negative Acknowledgment (NACK) signal to notify the transmitter that the decoded packet is erroneous. Having received the NACK signal, the transmitter may retransmit the packet.
FIG. 2 depicts a receiver for pilot-assisted communications. Since the receiver knows how the pilot and data symbols are mapped to the physical recourses, it can de-multiplex the received noisy signals associated with the transmitted pilot and data symbols. The received noisy signals associated with the transmitted pilot symbols are passed to the channel estimator that produces an estimate of the channel gain by utilizing the fact that it knows which pilot symbol are transmitted. Two typical examples of the channel estimators include maximum likelihood (ML) estimator and minimum mean square error (MMSE) estimator. The estimated channels are then passed to the decoder to perform decoding. For example, for iterative decoders, the estimated channel gains at the decoder are used to compute look-likelihood ratios (LLR) of transmitted bits using the received noisy data signals.
The purpose of transmitting pilot symbols is to enable the receiver to track the time-varying radio channel. The estimated channel gain can be used as side-information which allows the decoder to have an estimate of the received signals which is a noisy version of the transmitted coded modulated symbol perturbed by a phase rotation and magnitude amplification. For the time-varying channel, the received constellation is in general a scaled and rotated version of the transmitted signal constellation. The knowledge of the channel gain hence let the decoder operate with a lower block error rate (BLER) as compared to the case when there is no knowledge of the channel gain available prior to the decoding. The performance of the decoder for a given encoder-decoder pair depends on the quality of the channel estimator. The better the channel estimate is, the lower the BLER becomes which leads to a higher quality of service. The ultimate performance for a given encoder-decoder setup is to have a performance very close to the case with the perfect knowledge of the channel. However, in practice there is a loss in performance since the channel estimates might not be close to the true channel due the estimation noise. The channel estimation noise propagates to the decoder and this reduces the end-to-end performance.
Therefore, there is a need to design a communication system that generates channel estimation gains that does not deviate from the true channel gain experienced by the transmitted data. The imperfect channel estimation at the receiver degrades the performance of communication systems. The channel estimation noise; i.e. the estimation noise resulted for a channel estimator using the transmitted pilot symbols, propagates to the demodulator/decoder and spatial filters in case of Multiple-Input Multiple-Output, MIMO, links. The estimation noise reduces end-to-end performance, for example block error rate (BLER) or bit error rate (BER) for a given spectral efficiency or the maximum achievable spectral efficiency for given BLER or BER.
Several interesting results are reported by different researchers indicating a notable spectral efficiency loss as compared to that with perfect channel state information at the receiver. As a remedy, two main approaches are generally practiced: balancing the number of pilot and data symbols and optimizing the power allocation among these symbols. Both approaches only partially recover the spectral efficiency loss as compared to the case with perfect CSI. To resolve the channel estimation noise three main prior art solutions are considered.
Power Boosting
With power boosting, the transmitter transmits the pilot symbols with higher average power. This leads to lower channel estimation noise at the receiver. However, this strategy consumes power and is not suitable for the systems with limited peak-power constraints as most systems have a peak power constraint on the transmitted symbols. In addition to this drawback, the pilots with higher power in multi-user setups leads to a severe interference from the neighbouring transmitters when they transmit their pilot symbols or data symbols over the same time-frequency resource elements. One example is the pilot contamination which results from the interferences from the pilot symbols transmitted over the same time-frequency resource. Finally, for fast-varying channels the estimated channel gain using the pilot gets out-dated and hence the estimated channel gains become uncorrelated with the true one affecting the transmitted data symbols regardless how high the transmitted power of the pilot symbol is set.
Denser Pilot Transmissions
Alternatively, the transmitter can multiplex a higher number of pilot symbols for a given resource block. This solution also improves the quality of the channel estimation (i.e., it reduces the channel estimation noise) and is suitable for channels that vary fast since it produces the estimates that are more correlated to the actual channel gains experienced by the transmitted data symbols. The main shortcoming of this solution is that it reduces the spectral efficiency of the transmission as it consumes additional time-frequency resource elements for pilot transmission, which could be utilized for the data transmission.
Iterative Estimation-Decoding
Another way to improve the channel estimation quality is to use iterative demodulation/decoding and channel estimation. This type of strategy is suitable for decoders that are designed in an iterative manner. For such receivers, the decoder feedbacks its estimates of the data to the channel estimator and the channel estimators updates its estimate to be used by the decoder to update the log-likelihood ratio (LLR) values, see the dashed feedback from the decoder to the channel estimator in FIG. 2. This solution does not consume any physical resources but increases the complexity of the decoding as it requires additional outer loop iterations. More importantly, this solution suffers from error propagation since the feedback signals from the decoder are still noisy as the decoding is not yet completed. Finally, this solution may not be feasible for all type of decoders, for example those decoders that process the received noisy packet once and are not arranged for iterative estimation or those decoders that are designed to function using other metrics.